On downside risk predictability through liquidity and trading activity: a quantile regression approach
AbstractMost downside risk models implicitly assume that returns are a sufficient statistic with which to forecast the daily conditional distribution of a portfolio. In this paper, we address this question empirically and analyze if the variables that proxy for market liquidity and trading conditions convey valid information to forecast the quantiles of the conditional distribution of several representative market portfolios. Using quantile regression techniques, we report evidence of predictability that can be exploited to improve Value at Risk forecasts. Including trading- and spread-related variables improves considerably the forecasting performance.
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Bibliographic InfoPaper provided by Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie) in its series Working Papers. Serie AD with number 2011-14.
Length: 39 pages
Date of creation: Jun 2011
Date of revision:
Publication status: Published by Ivie
Value at Risk; Basel; Liquidity; Trading Activity.;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-07-02 (All new papers)
- NEP-FOR-2011-07-02 (Forecasting)
- NEP-MST-2011-07-02 (Market Microstructure)
- NEP-RMG-2011-07-02 (Risk Management)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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